Method and system for analyzing and assessing progress of a project

ABSTRACT

A system and method for determining the stability of a project. One embodiment includes computing at least two project progress parameters of a project for numerically describing elements of the project. Regression parameters are computed based upon the project progress parameters and correlation coefficients are computed utilizing the regression parameters. The correlation coefficients describe the strength of the correlation of the project progress parameters for indicating the stability of the project as project develops.

FIELD OF THE INVENTION

[0001] The present invention relates generally to development projectapplications, and, more particularly, but not by way of limitation, tothe application of statistical analysis techniques to establish metricsfor use in assessing progress of the project applications.

BACKGROUND OF THE INVENTION

[0002] In general, metrics have been used to perform such tasks asbusiness process assessment, software design analysis, and softwarecomplexity analysis. For example, metrics used for process assessmentare typically focused on measuring complexity and tracking processexecution times. Because there are limited metrics available thatprovide reliable tools for assessment of tasks, such as the developmentof business requirements specifications, the nature of businessrequirements specifications and other documentary summary data forproject applications has made identification of meaningful metricsproblematic. Moreover, the challenges of assessing the impacts ofproject components on the overall progress add to the aforementionedchallenges created by documentary summary data. It should be understood,of course, that each project team uses a different developmentalapproach for defining a requirements specification. For example, someuse waterfall, while others use spiral developmental approaches.Regardless of the developmental approach, many project developers usewriting methods learned early in their lives. Fundamentally, projectdevelopers develop an outline, followed by the content that brings theoutline to life. However, at the most fundamental level, the structureof most requirements specifications consists of branches 205 a-205 f,collectively 205, and leaves 210 a-210 f, collectively 210, asillustrated in FIG. 2A.

[0003] A leaf is, unambiguously, a subsection of a branch. The IEEE1998-830 standard, a widely used requirements template, provides anexample of this structure. For example, section “1.1.1” is a leaf ofsection “1.1”, assuming that there is no section “1.1.1.1”. Although notshown in FIG. 2A, each section has a title, e.g., a natural languagestructure would use a function name, an object oriented structure woulduse an object name, a requirements specification document would use asection title, and an accounting structure would use an account name.Furthermore, each branch 205 and leaf 210 may also include content, suchas text, numbers, images, etc., that define the specification document,accounting ledger, software program, or other document, for example.

[0004] Consequently, project managers tasked with managing arequirements specification, as previously stated, may lack reliablemethods, beyond raw experience, to successfully monitor the progress ofa project. As such, the issue of team synergy (where the individualgoal-directed actions are focused on the common end result, with a clearunderstanding of current reality in relation to the end result) and theimpacts of teamwork on project progress, regardless of the projectnature, has been unmeasurable, thus far. Hence, the inability to measureprogress of the project makes judging success extremely difficult.

SUMMARY OF THE INVENTION

[0005] To remedy the deficiencies of determining progress of projectapplications, the principles of the present invention provide a systemand method for employing statistical analysis techniques to quantify thesuccess of project applications by establishing metrics for use inassessing progress of a project application. The statistical analysistechniques use parameter dependencies to provide an operator of aproject the ability to assess the progress of the project application.Particularly, regression analysis provides an appropriate computationalsolution to quantify the success of the project application.Additionally, correlation coefficients derived from the regressionanalysis provide for determining the strength of the correlation of theparameter dependencies (e.g., age of branch and number of branchmodifications). The correlation coefficients provide the operator withconfidence that the statistical analysis is reliable.

[0006] One embodiment of the present invention provides a system andmethod for determining the stability of a project. This embodimentincludes computing at least two project progress parameters of a projectfor numerically describing elements of the project. Regressionparameters are computed based upon the project progress parameters andcorrelation coefficients are computed utilizing the regressionparameters. The correlation coefficients describe the strength of thecorrelation of the project progress parameters for indicating thestability of the project as it develops.

[0007] The project progress parameters may include, for example, totalnumber of leaves, number of modifications performed on the branches,number of modifications performed on the leaves, average age of leavesin the project, and average age of branches in the project. The projectprogress parameters are generated from performing statistical analysison collected project data and used as parameters in mathematicalequations to compute the regression parameters. These mathematicalequations may include normal equations used in regression analysis,slope equations of a regression model, intercept equations of theregression model, and correlation coefficient equations of theregression model.

BRIEF DESCRIPTION OF THE DRAWINGS

[0008] A more complete understanding of the principles of the presentinvention and the scope thereof is more readily appreciated by referenceto the following Detailed Description of the presently-preferredembodiments of the present invention and to the appended claims whentaken in conjunction with the accompanying Drawings wherein:

[0009]FIG. 1 is an exemplary network based system in which theprinciples of the present invention operate;

[0010]FIG. 2A is an exemplary block diagram of the project dataincluding branches and leaves;

[0011]FIG. 2B is an exemplary output from a project summary generationprocess according to FIG. 4 that describes the block diagram of FIG. 2A;

[0012]FIG. 3 is an exemplary flow diagram of a process for determiningthe stability of a project according to the principles of the presentinvention;

[0013]FIG. 4 is an exemplary flow diagram of the project summary datarecord generation process according to the principles of the presentinvention;

[0014]FIG. 5 is an exemplary flow diagram for analyzing summary datarecords as produced by the flow diagram 400 of FIG. 4;

[0015]FIG. 6 is an exemplary flow diagram of a long-term regressionanalysis process as produced by the flow diagram 500 of FIG. 5;

[0016]FIG. 7 is a plot comparison of hypothesized models versus actualdata collected from an example project resulting from the exemplary flowdiagram 500 of FIG. 5;

[0017]FIG. 8 is a plot comparison of a hypothesized model versus anactual correlation coefficient from regressing the branch modifications(BM) on the age of the branch (BA) of the example project according toFIG. 6;

[0018]FIG. 9 is a plot comparison of a hypothesized model versus anactual correlation coefficient from regressing the leaf modifications(LM) on the age of the leaf (LA) of the example project according toFIG. 6; and

[0019]FIG. 10 is a plot comparison of a hypothesized model versus anactual correlation coefficient from regressing the number of leaves (LN)produced per day to number of branches (BN) produced per day of theexample project according to FIG. 6.

DETAILED DESCRIPTION

[0020] Although the present invention is open to various modificationsand alternative constructions, a preferred exemplary embodiment that isshown in the above-referenced drawings is described herein in detail. Itis to be understood, however, that there is no intention to limit theinvention to the particular forms disclosed. One skilled in the art canrecognize that there are numerous modifications, equivalences andalternative constructions that fall within the spirit and scope of theinvention as expressed in the claims.

[0021] Data collected from a project is used to perform regressionanalysis to determine information about status of the project (i.e.,stability of a project's organization and content). Additionally,regression analysis and hypothesis testing are used to describe actualprogress of the project and to evaluate that progress in relation toexpected progress. These techniques allow project managers to assesscontributions of individual project team members and to forecastpossible need for additional or different project team resources.

[0022] Referring to FIG. 1, there is illustrated an exemplary networkbased system 100 having a server 105. The server 105 includes a memory110, a modem 115, and a microprocessor 120. The microprocessor 120collects project data by reading a data file 125 or a database 130through a data bus 135. The project data may be a requirements document,specification document, proposal document, request for proposaldocument, sales performance, manufacturing process, accounting system,distribution system, or software development, for example. The projectdata is used by the microprocessor 120 during execution of projectanalysis tools 140, including project process assessment tools 141 andstatistical analysis tools 142, to generate statistical data such asproject progress parameters, regression parameters, and correlationcoefficients. Alternatively, the microprocessor 120 can collect projectdata from a communication network 145 via data packets 150 transmittedover a wired or wireless interface 155.

[0023] It should be understood, of course, that the aforementioneddatabase can be represented as a single database 130 or as separatedatabases 130 a, 130 b, and 130 c. The databases 130 a-130 c are createdand updated using the project analysis tools 140 and are used forproject progress analysis. For example, the project process assessmenttools 141 are used to parse the requirements document data.Subsequently, the project process assessment tools 141 causes rawproject data to be stored in the project summary data records database130 a, thereby creating or updating a repository of daily projectsummary data records 160 a, shown as a single project summary datarecord 160 b in FIG. 2B. Moreover, the statistical analysis tools 142are used to perform statistical analysis on the project summary datarecords 160 a contained in database 130 a. Accordingly, project progressparameters are generated and the project process assessment tools 141are used to store the project progress parameters in database 130 b.Thus, a repository of daily project progress parameters 165 is createdor updated, shown as a single record in Table 1. Furthermore, thestatistical analysis tools 142 are used to perform regression analysison the project progress parameters 165 contained in database 130 b.Thus, regression parameters are generated and used to populateregression models. Following, the project process assessment tools 141are used to store the regression models in database 130 c, therebycreating or updating a repository of long-term regression analysisresults, shown as a single record in Table 2.

[0024] It should be further understood that a user can operate within asystem environment using a terminal 170 coupled to the server 105 toimplement the principles of the present invention. Alternately, the usercan operate outside of the system environment using a remote terminal175 and server 180 connected to the communication network 145 toimplement the principles of the present invention.

[0025] Further referring to FIG. 2A, there is illustrated an exemplaryblock diagram 200 a of the project data organized in a representativetree structure including branches 205 and leaves 210, in which thebranches 205 represent structure components of the requirements documentand the leaves 210 represent content components of the requirementsdocuments. Additionally, an alternate embodiment of the presentinvention operates on documents configured in a content markup fashion.Particularly, markup defines a hierarchical structure for text or data.In the case of XML (extensible Markup Language), and other markuplanguages, the markup defines a tree structure. Consequently, markup isconcerned with the logical structure and content of the text or data,e.g., to indicate sections, subsections, and headers in a documentsimilar to the branch and leaf structure. For example, an XML documentwould be formatted in the following manner: <document><header#><1.1></header#> <title><title of paragraph 1.1></title><content><Hello World!></content> </document>.

[0026] Referring to FIG. 2B, the expressiveness of the branch and leafstructures can be developed using an incremental approach. For example,for every branch 205 a′-205 f′, collectively 205′, and leaf 210 a′-210f′, collectively 210′, there is included an object ID 215, object number220, level in document 225, branch or leaf entry 230, date of creation235, number of modifications 240, modification status 245, user name250, and age of artifact 255. Accordingly, leaf and branch metrics(i.e., graphics indicating analysis results generated from usingregression statistics computed to assess progress of a project) providedby the principles of the present invention help project managersunderstand the progress of this development pattern.

[0027] Referring to FIG. 3, there is illustrated an exemplary flowdiagram 300 of a process for determining the stability of a project,configured in accordance to the principles of the present invention. Theprocess starts at step 305. At step 310, the project summary datarecords (e.g., branch and leaf data 205 and 210) are used to performstatistical analysis to compute project progress parameters, whichnumerically describe elements of the project. Basically, the projectelements are branch and leaf objects contained within a “super artifact”as illustrated in FIG. 2B. The project progress parameters representvarious project characteristics and may be stored in a database. Theproject progress parameters may include: total number of branches, totalnumber of leaves, number of modifications performed on the branches,number of modifications performed on the leaves, average age of leavesin the project, and average age of branches in the project, for example.

[0028] At step 315, the project progress parameters are used to performregression analysis to compute regression parameters. This processemploys statistical equations used to perform regression analysis, i.e.,normal equations (eqns. 1-3) used in regression analysis, including (i)slope (eqn. 4) of the regression model equation, (ii) intercept (eqn. 5)of the regression model equation, (iii) correlation coefficient (eqn. 6)of regression equation, and (iv) alternate equation for the correlationcoefficient (eqn. 7) of regression, to compute the regressionparameters, develop models of the project development process, andassess the strength of the relationships being analyzed with the metricsthat have been established. These equations can be expressed as:

S _(XX) =Σ _(i) ²−(ΣX _(i))² /n  (1)

S _(YY) ΣY _(i) ²−(ΣY _(i))² /n  (2)

S _(XY) =XY _(i)−(ΣX _(i))(ΣY _(i))/n  (3)

b ₁ =S _(XY) /S _(XX)  (4)

b ₀ ={overscore (Y)}−b ₁ {overscore (X)}  (5)

R ² =b ₁ S _(XY) /S _(YY)  (6) $\begin{matrix}{r = {\frac{\sum{\left( {X_{i} - \overset{\_}{X}} \right)\left( {Y_{i} - \overset{\_}{Y}} \right)}}{\left( {\sqrt{\sum\left( {X_{i} - \overset{\_}{X}} \right)^{2}}\sqrt{\sum\left( {Y_{i} - \overset{\_}{Y}} \right)^{2}}} \right.}.}} & (7)\end{matrix}$

[0029] The regression parameters (i.e., slope, intercept, correlationcoefficient) populate a third data record that includes statisticalresults indicative of the development of the project. At step 320, theregression parameters are used to compute correlation coefficients. Atstep 325, the process ends.

[0030] The correlation coefficients, when taken together with projectsummary data records, paint an informative picture about the stabilityof the project, i.e, the strength of the relationships betweenindependent variables and dependent variables. Additionally, thecorrelation coefficients are used to populate regression models.Fundamentally, these regression models provide project managers with ameans for comparing idealized or estimate curves with actual curves(FIGS. 7-10) in order to adjust their tactical objectives appropriately.Principally, regression analysis is performed to facilitate dailyproject progress assessments as well as forecast the need for additionalresources, whereas, long-term regression analysis is performed in orderto accomplish long-term project assessment.

[0031] Referring to FIG. 4, there is illustrated an exemplary flowdiagram 400 of the project summary data record or artifact generationprocess according to the principles of the present invention. Theprocess starts at step 405. At step 410, project data is collectedeither by reading a project data file 125 or databases 130 a-130 c,collectively 130, on the server computer 105. Alternately, the projectdata can be received from a communication network 145 via data packets150. At step 415, the project data is parsed to extract pertinent datafor generating branch and leaf data 205 and 210 (i.e., artifacts) toimplement project progress analysis. The project data is summarized 160b as illustrated in FIG. 2B and can be used for the project analysisusing regression analysis as described by equations (1)-(7).Particularly, artifacts can be used for the analysis of the project upto the day the analysis is performed. At step 420, the artifacts aresaved to a project summary data records database 130 a, which is arepository 160 a updated with artifact objects (i.e., project summarydata records). At step 425, the process ends.

[0032] Referring to FIG. 5, there is illustrated an exemplary flowdiagram 500 for analyzing summary data records as produced by the flowdiagram 400 of FIG. 4. The process starts at step 505. At step 510project summary data records 160 a are accessed from the project summarydata records database 130 a. At step 515, the project summary datarecords are used to perform statistical analysis. At step 520, theproject progress parameters are saved to a project progress parametersdatabase 130 b, which is a repository 165 updated with project progressparameters. At step 525, the process ends. Accordingly, Table 1represents exemplary project progress parameters from performingstatistical analysis on the project summary records 160 a. It should beunderstood that other desired project progress parameters could begenerated. TABLE 1 Project Progress Parameters DATE; (27 7 2000)AVG-BRANCH-AGE; 665.665620 SD-BRANCH-AGE; 29.875499 AVG-LEAF-AGE;667.192628 SD-LEAF-AGE; 0 AVG-MODIFIED-BRANCHES; 22.642857SD-MODIFIED-BRANCHES; 44.936891 AVG-MODIFIED-LEAVES; 88.785714SD-MODIFIED-LEAVES; 180.132079 AVG-BRANCH- 3.660910 MODIFICATIONS;SD-BRANCH-MODIFICATIONS; 4.892677 AVG-LEAF-MODIFICATIONS; 1.827304SD-LEAF-MODIFICATIONS; 3.134201 TOTAL-BRANCHES; 637 TOTAL-LEAVES; 3179AVG-BRANCHES; 45.5 SD-BRANCHES; 65.055893 AVG-LEAVES; 227.071428SD-LEAVES; 326.246123

[0033] Referring to FIG. 6, there is illustrated an exemplary flowdiagram 600 of a long-term regression analysis process as produced bythe flow diagram 500 of FIG. 5. The process starts at step 605. At step610, project progress parameters 165 (e.g., Table 1) are accessed fromthe project progress parameters database 130 b. At step 615, the projectprogress parameters 165 are used to perform a long-term regressionanalysis. In particular, this process uses the project progressparameters 165 to compute regression parameters using equations (1)-(3)as previously described above to generate the long-term regressionanalysis data. At step 620, the long-term regression analysis data issaved or updated to a long-term regression analysis database 130 c. Theprocess ends at step 625. Table 2 represents the resulting long-termregression parameters from performing the long-term regression analysis(step 615) on the project progress parameters 165. TABLE 2 RegressionParameters Regression Model: TOTAL-LEAVES-ON-TOTAL-BRANCHES slope22.3333333333333 intercept −2494.66666666667 correlation coefficient 1.0Regression Model: AVG-LEAF-AGE slope 2.25261845640354E−14 intercept107.480733944954 correlation coefficient 2.11182980287832E−15 RegressionModel: AVG-BRANCH-AGE slope 2.25261845640354E−14 intercept186.142857142857 correlation coefficient 2.11182980287832E−15 RegressionModel: TOTAL-LEAVES-ON-TOTAL-BRANCHES slope 4.19907168643631 intercept−41.8422726491319 correlation coefficient 0.641106895479179

[0034] The metrics described hereinafter allow project managers tomonitor the operational progress of a project. Additionally, the 20principles of the present invention analyze the progress of the projecton a tactical and strategic basis. These terms (i.e., operational,tactical, strategic) are based on the systematic approach set forth inthe U.S. military publication, entitled “A Tactical Evolution-FM 100-5”and have been modified for business purposes, as discussed in detailbelow.

[0035] In business terms, tactical progress is the management ofresources to achieve tactical objectives, which summarily supportsoperational progress. For example, generally speaking, tactical progresscan be defined as the type and amount of work completed each day. Thus,tactical objectives define the type and amount of work team members areexpected to complete daily. Specifically, tactical objectives may be setwhich direct some team members to focus on branch work, and others tofocus on leaf work. Based on the type of work, some team members may bedirected to work on leaves in parallel, while other team members workserially on branches.

[0036] Moreover, in business terms, operational progress is theemployment of resources to attain strategic objectives through thedesign, organization, integration, and execution of tactical measures.For example, generally speaking, operational progress can be defined asthe summation of tactical progress on a weekly basis. Thus, operationalobjectives are based on branch and leaf metrics. Specifically, idealizedcurves (scaled for a specified schedule) are compared with actual curvesand tactical objectives are adjusted appropriately.

[0037] Furthermore, in business terms, strategic progress is the art ofmanaging tactical and operational progress to complete an engagement(e.g., a requirements specification) on time, within budget, and withinsome previously defined parameters. For example, generally speaking,strategic progress can be defined as the summation of operationalprogress. Thus, strategic objectives relate to cost, effort, andschedules. Specifically, the development of the project is monitored inrelation to the strategic objectives, and operational and tacticaladjustments are made accordingly.

[0038] Referring to FIG. 7, there is illustrated a plot comparison 700of hypothesized models versus actual data collected from an exampleproject resulting from the exemplary flow diagram 500 of FIG. 5. Asdiscussed above, most project developers first construct a requirementsspecification outline before proceeding with defining the content of thesections within the outline. This pattern of development explains thepopularity of templates (for example, IEEE 1998-830) that predefine theoutline. Those skilled in the art recognize that it is very easy tospend too long perfecting an outline, or languish trying to find anoutline that works for a given project. By examining the ratio of thenumber of branch modifications over the life of a project (BM) versusthe average branch age in days (BA), project managers can assess thestability of the structure of project application, such as arequirements specification.

[0039] Logically, if a project team does not start with a template, aproject manager should expect a fast growing population of youngbranches with a large number of modifications. Thus, the ratio 701 ofthe BM to BA initially increases as seen by curves 701 a and 701 bbetween days 1-3. However, at some point in time, the increase in BMslows as the project manager finds a structure that works for theproblem that the project team specifies. Furthermore, as the projectmanager's satisfaction with the structure increases, the project teamintroduces fewer branches, leading to an increasing BA as the branchpopulation ages. Thus, the ratio quickly heads towards zero. Curve 701 adepicts the logical representation of this assumption or hypothesis.

[0040] For example, point S₁ represents the start of the project.Logically, point S₁ is initially undefined, as all the branches have aninitial age of zero. Furthermore, point S₂ represents the point at whichthe project developer determines the specification structure sufficesfor the problem at hand with modifications. Finally, point S₃ representsthe end of the project.

[0041] Examining the relationships between the points leads toadditional comments and determinations a project manager can make aboutspecification structure. Particularly, the area beneath the curve linebetween points S₁ and S₂ represents the energy required to build thestructure. If the project developer uses a template estimated asappropriate for the problem, then the distance between points S₁ and S₂should equal zero, enabling the project developer to simply add contentwithout having to create structure.

[0042] As stated above, the area beneath the curve line between pointsS₂ and S₃ represents the energy or effort required to adjust the initialstructure to match the actual appropriateness of the template to theproblem. Accordingly, the smaller the area below the curve 701 a betweenS₂ and S₃, the more appropriate the initially chosen structureillustrates. To further assess the stability of the structure model,curve 701 b, representing actual data collected from the sample projectdiscussed above, is included in the plot 700. The curve 701 b reflectsthe project team's initial estimate that the structure developed by daythree was appropriate for the problem the project team faced. Forexample, each segment of the curve 701 b with a positive sloperepresents the project team reassessing the actual appropriateness ofthe initial structure and adjusting the structure to better fit theproblem. Interestingly, the dip between day 10 and day 11 represents anappendix (new structure component) to the project that the project teamintroduced late in the project.

[0043] Once project developers realize a structure that appears to work,they can progress with defining the content that ultimately defines theproblem in detail. Unfortunately content definition is another task thatoften challenges project developers. While project developers oftensuffer from writer's block, a more common problem is understanding whento stop defining content. It should be understood that content for arequirements specification project is text and/or graphics, whilecontent for a software application project is source code.

[0044] Due to the common problem of not understanding when to stopdefining content, the energy expended on content definition is theprimary contributor to slipping schedules. Therefore, by examining theratio 702 of the number of leaf modifications over the life of a project(LM) versus the average leaf age in days (LA), project managers canassess content stability for a specification.

[0045] Logically, assuming the project team does not start with atemplate, a project manager expects an increasing population of new,young leaves with few modifications as the project team stabilizes thestructure. Leaves may become branches or future branches during projectdevelopment. Thus, at the beginning of the project, the ratio 702 of LMto LA gradually increases at a slightly faster rate than the ratio 701of BM to BA because the focus is initially on structure. However, atsome point after point S₂ described above, the project manager expectsthe project team to shift its focus from structure definition to contentdefinition. At this point, generally speaking, the project developersintroduce multiple leaves per branch. Moreover, at some point, theproject team stops producing new content and works with projectreviewers to ensure that the existing material is ready for endorsement.Curve 702 a depicts the logical representation of this assumption.

[0046] For example, point C₁ represents the start of the project.Similar to structure stability, point C₁ is undefined as the averageleaf age is zero. The ratio C₂/S₂ represents the point at which theproject developer determines that the specification structure sufficesfor the problem at hand and shifts focus to content. Furthermore, pointC₃ represents the point at which the project manager and projectreviewers agree that the content sufficiently represents the problem,but requires modifications. Finally, point C₄ represents the end of theproject.

[0047] Examining the relationships between the points leads toadditional comments and determinations a project manager can make aboutcontent. Particularly, the area under the curve between points C₂ and C₃represents the energy or effort required to specify the problem detail(i.e., to add content to the structure of the project). It should, ofcourse, be understood to those skilled in the art that, collectively,elicitation methods (e.g., induction), documentation methods, andrequirements reuse work to reduce this area.

[0048] However, the area beneath the curve line between points C₃ and C₄represents the energy or effort required to adjust (i.e., edit) thedetail to appropriately define the problem detail. Clearly, employingreview methods help to reduce this area. To further assess the stabilityof the content model, curve 702 b, representing actual data collectedfrom the aforesaid sample project, is included in the plot 700. Thecurve 702 b reflects the project team's initial challenge to obtain astructure that enables managing a problem. Consequently, around day 10,the project team realizes a structure and works long hours over the nextfew days in order to make up for the time lost establishing thestructure. In effect, the project team uses the LM to LA ratio 702 todetermine the proximity of points C₂ and C₃, which the project team usesto ensure it realizes the delivery date and to maximize review time.

[0049] The description of content stability and structure stability, asdiscussed above, can be combined by examining the ratio 703 of thenumber of leaves for a given day (LN) to the number of branches for thesame day (BN) in order to study production stability. Logically, aproject manager expects an initial increase in the number of branchesand future branches (initially appearing as leaves) as the structurebecomes increasingly unstable. Thus, the ratio 703 of LN to BN hoversaround a constant, approaching one as illustrated by curves 703 a and703 b. Once the project team feels the structure is appropriate, theproject manager expects the content stability to increase as the projectteam focuses on creating leaves to capture problem detail. Thus, theratio 703 of LN to BN sharply increases. After the project team developsthe raw material and both the content and structure stability increase,the project manager expects the LN to BN ratio 703 to stabilize andremain constant. Curve 703 a depicts the logical representation of theseassumptions.

[0050] For example, point P1 represents the start of the project. As inthe other models, point P1 is technically undefined because the numberof branches and number of leaves both equal zero at the start of aproject. Furthermore, ratio P2/S2 represents the point at which thecontent stabilizes and defining the content takes precedence. Moreover,ratio P3/C3 represents the point at which the specification containsenough material to represent the problem in the view of both the projectteam and the reviewers. Finally, point P4 represents the end of theproject.

[0051] Examining the relationships between the points leads toadditional comments and determinations a project manager can make aboutproduction stability. Particularly, the number of days between points P2and P3 represents the rate at which the project team can identify theproblem detail.

[0052] As discussed, the distance between points P2 and P3 representsthe granularity of the branches. For example, the smaller the distance,the more finely grained the structure (i.e., negligible transitionpoints), potentially decreasing a reviewer's ability to understand aspecification. Alternatively, a very large distance represents astructure with coarse granularity (i.e., extensive transition points),also presenting a potential risk of lowering a reviewer's ability tounderstand a specification. To further assess the stability of theproduction model, curve 703 b, representing actual data collected fromthe sample project discussed above, is included in the plot 700. Thecurve 703 b reflects the project team's ability to produce the rawmaterial required to specify the problem, however, the stability curvesabove, indicate that the material produced suffers from prolongedperiods of instability. In other words, production was not the problem,but adjusting the material produced to meet the client's needs was aproblem. Interestingly, the dip between day 10 and day 11 represents anappendix (new production component) to the project that the project teamintroduced late in the project, described in the structure stabilitysection above.

[0053] It should, of course, be understood to those skilled in the artthat the above-mentioned metrics taken alone may not provide individualpower greater than the power provided by all of the metrics takentogether. However, viewed together in the plot 700, the metrics paint aninformative picture about the progress of a project.

[0054] Referring to FIG. 8, there is illustrated a plot comparison of ahypothesized model versus an actual correlation coefficient fromregressing the branch modifications (BM) on the age of the branches (BA)of the example project, according to FIG. 6. Based on the abovediscussion of the structure stability metric, it is understood that thenumber of modifications a project developer makes to a branch isdependent on the age of the branch.

[0055] Logically, if these dependencies exist, a project managerinitially expects to see a strong negative correlation between thenumber of modifications to a branch and its age because the projectdeveloper makes a large number of modifications to a branch on thebranch's first day of existence. Next, the project manager expects thecorrelation to head towards zero on its way to positive as the projectdeveloper makes more modifications. Eventually, the correlation headsback towards a strong negative correlation as the project developerstops making modifications to the aging branch. Accordingly, curve 800 adepicts an idealized or hypothesized representation of theseassumptions. Additionally, curve 800 b, representing the actualcorrelation between the number of branch modifications and thecorresponding branch age, is included in plot 800 to further assess theappropriateness of the project team's initially asserted assumptions.

[0056] Referring to FIG. 9, there is illustrated a plot comparison of ahypothesized model versus an actual correlation coefficient fromregressing the leaf modifications (LM) on the age of the leaf (LA) ofthe example project, according to FIG. 6. Logically, the correlationcoefficient resulting from a regression analysis of the number of leafmodifications on the age of the leaf is similar to the correlationcoefficients that accompany the structure stability metric, but isshifted to account for the delayed attention to content. Thus, a projectmanager expects to initially see a strong negative correlation betweenthe number of modifications to a leaf and its age as the leaves and agegrow together. Because the structure is the primary initial focus,project managers should expect an initially weaker correlationcoefficient for content than structure. Once the structure stabilizes,the correlation coefficient for content shoots towards zero on its wayto positive as the project developer shifts focus to content. Moreover,once the content stabilizes, project managers expect the correlationcoefficient to dive negatively as leaves age, but experience fewermodifications. Accordingly, curve 900 a depicts an idealized orhypothesized representation of these assumptions. Additionally, curve900 b, representing the actual correlation between the number of leafmodifications and the corresponding leaf age, is included in plot 900 tofurther assess the appropriateness of the project team's initiallyasserted assumptions.

[0057] Referring to FIG. 10, there is illustrated a plot comparison of ahypothesized model versus an actual correlation coefficient fromregressing the number of leaves (LN) produced per day to number ofbranches (BN) produced per day of the example project, according to FIG.6. Logically, if the production stability assumptions stated above arecorrect, project managers expect the correlation coefficient of theregression of the number of leaves produced per day on the number ofbranches produced per day to, initially, be very highly correlated asbranches and leaves are produced at a similar rate. Moreover, as thestructure stabilizes, project managers expect to see the correlationcoefficient head towards negative one (−1) as leaves increase at a muchgreater rate than branches. Accordingly, curve 1000 a depicts anidealized representation of these assumptions. Additionally, curve 1000b, representing the actual correlation between the number of leavesproduced per day and the number of branches produced per day, isincluded in plot 1000 to further assess the appropriateness of theproject team's initially asserted assumptions.

[0058] Although the presently preferred embodiment is directed towardsystems and methods that facilitate project developers in theapplication of statistical analysis techniques and hypothesis testing toestablish metrics for use in assessing project progress, it should beunderstood that the principles of the present invention may beimplemented to perform any analysis process where there is a potentialfor the value of one variable to be dependent on the value of anotherand the project regression analysis process is merely a current example.

[0059] As will also be recognized by those skilled in the art, theinnovative concepts described in the present application can be modifiedand varied over a wide range of applications. For example, although theembodiments implement project progress analysis, the invention is notlimited to such a process and can be practiced in other general businessprocesses, such as Sales Performance Assessment, Manufacturing ProcessAssessment, and Distribution System Performance Evaluation. Accordingly,the scope of the present invention should not be limited to any of thespecific exemplary teachings discussed, but is limited by the followingclaims.

What is claimed is:
 1. A method for determining stability of a project,the method comprising: computing at least two project progressparameters based upon project summary data of a project for numericallydescribing elements of the project; computing regression parametersbased upon the at least two project progress parameters; and computingcorrelation coefficients utilizing the regression parameters, thecorrelation coefficients describing the strength of the correlation ofthe at least two project progress parameters for indicating thestability of the project.
 2. The method of claim 1, wherein the projectprogress parameters include at least one of the following: total numberof branches, total number of leaves, number of modifications performedon the branches, number of modifications performed on the leaves,average age of leaves in the project, and average age of branches in theproject.
 3. The method of claim 1, wherein the stability of the projectis determined by utilizing at least one of the following equations:normal equations used in regression analysis, slope of the regressionmodel equation, intercept of the regression model equation, andcorrelation coefficient of the regression equation.
 4. The method ofclaim 1, further comprising the steps of: collecting data of theproject, the data being structured as branches and leaves; and updatingat least one database with data records generated from performingstatistical analysis on the collected data.
 5. The method of claim 4,wherein the collecting of data includes at least one of the followingsteps: reading data from a data file or database; or receiving dataacross a network.
 6. The method of claim 4, wherein the branches arerepresentative of structure components of the requirements document, andthe leaves are representative of content components of the requirementsdocument.
 7. The method of claim 1, further comprising outputting thedata records to graphically represent the stability of the project. 8.The method of claim 1, wherein the project includes at least one of thefollowing: a requirements document, a specification document, a proposaldocument, a request for proposal document, a sales performance document,a manufacturing process, an accounting system, a distribution system,and a software development project.
 9. A method for analyzingdevelopment of a project, comprising: collecting data of the project,the data structured as branches and leaves; parsing the data of theproject to produce first data records summarily describing the data ofthe project; computing second data records based on the first datarecords, the second data records including statistical data describingthe first data records; and computing third data records, the third datarecords including statistical results based upon the second data recordsand being indicative of the development of the project.
 10. The methodof claim 9, wherein the collecting of data includes at least one of thefollowing steps: reading data from a data file or database; or receivingdata across a network.
 11. The method of claim 9, wherein the second andthird data records are stored in a database.
 12. The method of claim 9,wherein the third data records are computed using regression analysis,the regression analysis being performed to facilitate daily projectprogress assessments and forecast the need for additional resources. 13.The method of claim 9, wherein the statistical results are timedependent.
 14. The method of claim 9, wherein the third data recordshave a variable dependency.
 15. The method of claim 9, furthercomprising outputting at least one of the following: the second andthird data records.
 16. The method of claim 9, wherein the first,second, and third data records are structured as objects.
 17. The methodof claim 9, wherein the project is formatted on at least one of thefollowing formats: object oriented format, and content markup languageformat.
 18. The method of claim 9, further comprising computingcorrelation coefficients based upon the third data records.
 19. Themethod of claim 9, wherein the project includes at least one of thefollowing: a requirements document, a specification document, a proposaldocument, a request for proposal document, a sales performance document,a manufacturing process, an accounting system, a distribution system,and a software development project.
 20. A system for determiningstability of a project, the system comprising: at least a firstprocessor for executing processes; at least a first memory deviceconnected to the at least first processor; and a plurality of processesstored on the at least a first memory device, the plurality of processesconfigured to cause the at least first processor to: compute at leasttwo project progress parameters based upon summary data of a project fornumerically describing elements of the project; compute regressionparameters based upon the at least two project progress parameters; andcompute correlation coefficients utilizing the regression parameters,the correlation coefficients describing the strength of the correlationof the at least two project progress parameters for indicating thestability of the project.
 21. The system of claim 20, wherein theproject progress parameters include at least one of the following: totalnumber of branches, total number of leaves, number of modificationsperformed on the branches, number of modifications performed on theleaves, average age of leaves in the project, and average age ofbranches in the project.
 22. The system of claim 20, wherein thestability of the project is determined by utilizing at least one of thefollowing equations: normal equations used in regression analysis, slopeof the regression model equation, intercept of the regression modelequation, and correlation coefficient of the regression equation. 23.The system of claim 20, wherein the plurality of processes are furtherconfigured to cause the at least a first processor to: collect data ofthe project, the data being structured as branches and leaves; andupdate at least one database with data records generated from performingstatistical analysis on the collected data.
 24. The system of claim 23,wherein the at least first processor further collects data by performingat least one of the following: reading data from a data file ordatabase; or receiving data across a network.
 25. The system of claim23, wherein the branches are representative of structure components ofthe requirements document, and the leaves are representative of contentcomponents of the requirements document.
 26. The system of claim 20,wherein the plurality of processes are further configured to cause theat least a first processor to: output the data records to graphicallyrepresent the stability of the project.
 27. The system of claim 20,wherein the project includes at least one of the following: arequirements document, a specification document, a proposal document, arequest for proposal document, a sales performance document, amanufacturing process, an accounting system, a distribution system, anda software development project.
 28. A system for determining stabilityof a project, the system comprising: means for computing at least twoproject progress parameters based upon project summary data of a projectfor numerically describing elements of the project; means for computingregression parameters based upon the at least two project progressparameters; and means for computing correlation coefficients utilizingthe regression parameters, the correlation coefficients describing thestrength of the correlation of the at least two project progressparameters for indicating the stability of the project.